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Record W2745214708 · doi:10.1161/str.47.suppl_1.tp306

Abstract TP306: Characteristics and Outcomes Among Patients Transferred to Regional Stroke Centers Across the United States for Specialized Stroke Care

2016· article· en· W2745214708 on OpenAlexaff
Syed F. Ali, Gregg C. Fonarow, Eric E. Smith, Li Liang, Robert Sutter, Ying Xian, Eric D. Peterson, Deepak L. Bhatt, Lee H. Schwamm

Bibliographic record

VenueStroke · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineStroke (engine)Odds ratioEmergency medicineOddsAcute strokePediatricsInternal medicineLogistic regression

Abstract

fetched live from OpenAlex

Intro: Many patients are transferred to stroke centers for advanced stroke care, especially after IV tPA. We sought to determine differences in the baseline characteristics and outcomes between AIS cases presenting directly to stroke centers’ front doors vs. transfers-in from another regional acute care hospital. Methods: Using data from the national GWTG-Stroke registry, we analyzed 970,390 AIS cases (01/2010 - 03/14). Patients at hospitals with high transfer-in rates (>15%) were selected (284 hospitals, 303,739 patients). Due to large sample size, instead of p-values, standardized differences were reported. Multivariable model (MV) examined the association of transfer-in vs. front door with the primary and secondary outcomes, adjusting for patient and hospital characteristics including NIHSS. Results: High volume transfer-in hospitals admitted 31% of their patients via transfer. Transfer-in patients were younger, more often white and non-Hispanic. They had similar stroke risk factors except for hypertension and previous stroke/TIA which were less common. Transfer-in had worse initial NIHSS, more often had altered consciousness and language disturbance. Transfer-in patients had longer length of hospital stay, higher mRS at discharge, and were less often discharged home. In-hospital mortality was ∼ 3% higher in transfer-in as compared with front-door. Among tPA treated patients, sICH < 36hr was more common in transfer-in patients. On MV, transfer-in patients had overall worse outcomes as shown by the higher odds of in-hospital mortality, longer length of stay, and not able to ambulate independently at discharge (Table). Conclusion: Many hospitals receive high volumes of stroke patients via transfer. Because transfer-in patients have worse outcomes, these patients have the potential to negatively influence institutional outcomes rates. Transfer-in patients should be carefully accounted for in risk adjusted models of hospital outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.282
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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